Pandas Profiling Report

Dataset statistics

Number of variables	37
Number of observations	234
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	67.8 KiB
Average record size in memory	296.5 B

Variable types

Categorical	27
Numeric	10

Alerts

`Job Title` has a high cardinality: 127 distinct values	High cardinality
`Founded` is highly correlated with `Size` and 2 other fields	High correlation
`Lower Salary` is highly correlated with `Rating` and 5 other fields	High correlation
`Upper Salary` is highly correlated with `Industry` and 5 other fields	High correlation
`Avg Salary(K)` is highly correlated with `Rating` and 7 other fields	High correlation
`Age` is highly correlated with `Founded` and 4 other fields	High correlation
`spark` is highly correlated with `Python` and 1 other fields	High correlation
`pytorch` is highly correlated with `scikit` and 1 other fields	High correlation
`tensor` is highly correlated with `keras` and 2 other fields	High correlation
`hadoop` is highly correlated with `spark`	High correlation
`Min_Salary` is highly correlated with `Hourly` and 6 other fields	High correlation
`Max_Salary` is highly correlated with `Industry` and 7 other fields	High correlation
`Min_Revenue` is highly correlated with `Size` and 4 other fields	High correlation
`Max_Revenue` is highly correlated with `Size` and 4 other fields	High correlation
`Type of ownership` is highly correlated with `Industry` and 6 other fields	High correlation
`job_title_sim` is highly correlated with `Industry` and 5 other fields	High correlation
`Python` is highly correlated with `Industry` and 2 other fields	High correlation
`Hourly` is highly correlated with `Type of ownership` and 3 other fields	High correlation
`Industry` is highly correlated with `Rating` and 14 other fields	High correlation
`Sector` is highly correlated with `Size` and 8 other fields	High correlation
`google_an` is highly correlated with `Industry` and 1 other fields	High correlation
`Job Location` is highly correlated with `Size` and 8 other fields	High correlation
`sql` is highly correlated with `Industry` and 2 other fields	High correlation
`Rating` is highly correlated with `Industry` and 3 other fields	High correlation
`Size` is highly correlated with `Founded` and 5 other fields	High correlation
`Employer provided` is highly correlated with `Rating` and 5 other fields	High correlation
`keras` is highly correlated with `tensor`	High correlation
`scikit` is highly correlated with `pytorch` and 1 other fields	High correlation
`tableau` is highly correlated with `bi`	High correlation
`bi` is highly correlated with `tableau`	High correlation
`seniority_by_title` is highly correlated with `Avg Salary(K)`	High correlation
`Degree` is highly correlated with `job_title_sim`	High correlation

Reproduction

Analysis started	2022-10-02 04:34:42.398811
Analysis finished	2022-10-02 04:35:22.395935
Duration	40 seconds
Software version	pandas-profiling v3.3.0
Download configuration	config.json

Job Title
Categorical

HIGH CARDINALITY

Distinct	127
Distinct (%)	54.3%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

Data Scientist	51
Data Engineer	23
Senior Data Scientist	11
Data Analyst	10
Senior Data Engineer	7
Other values (122)	132

Length

Max length	98
Median length	65
Mean length	24.63675214
Min length	9

Characters and Unicode

Total characters	5765
Distinct characters	62
Distinct categories	8 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	116 ?
Unique (%)	49.6%

Sample

1st row	Data Scientist
2nd row	Healthcare Data Scientist
3rd row	Data Scientist
4th row	Data Scientist
5th row	Data Scientist

Common Values

Value	Count	Frequency (%)
Data Scientist	51	21.8%
Data Engineer	23	9.8%
Senior Data Scientist	11	4.7%
Data Analyst	10	4.3%
Senior Data Engineer	7	3.0%
Senior Data Analyst	5	2.1%
Scientist	3	1.3%
Clinical Data Analyst	2	0.9%
Research Scientist	2	0.9%
Sr. Data Engineer	2	0.9%
Other values (117)	118	50.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
data	193	24.2%
scientist	127	15.9%
engineer	52	6.5%
analyst	42	5.3%
senior	39	4.9%
	38	4.8%
sr	11	1.4%
analytics	11	1.4%
science	9	1.1%
research	8	1.0%
Other values (168)	268	33.6%

Most occurring characters

Value	Count	Frequency (%)
t	647	11.2%
a	633	11.0%
	564	9.8%
i	530	9.2%
e	485	8.4%
n	474	8.2%
s	276	4.8%
c	254	4.4%
S	229	4.0%
r	225	3.9%
Other values (52)	1448	25.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	4261	73.9%
Uppercase Letter	813	14.1%
Space Separator	564	9.8%
Other Punctuation	58	1.0%
Dash Punctuation	35	0.6%
Decimal Number	12	0.2%
Open Punctuation	11	0.2%
Close Punctuation	11	0.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	647	15.2%
a	633	14.9%
i	530	12.4%
e	485	11.4%
n	474	11.1%
s	276	6.5%
c	254	6.0%
r	225	5.3%
l	155	3.6%
o	144	3.4%
Other values (15)	438	10.3%

Uppercase Letter

Value	Count	Frequency (%)
S	229	28.2%
D	206	25.3%
A	85	10.5%
E	62	7.6%
C	33	4.1%
M	32	3.9%
I	28	3.4%
P	26	3.2%
L	22	2.7%
R	16	2.0%
Other values (13)	74	9.1%

Other Punctuation

Value	Count	Frequency (%)
,	25	43.1%
/	17	29.3%
.	8	13.8%
&	7	12.1%
:	1	1.7%

Decimal Number

Value	Count	Frequency (%)
2	5	41.7%
0	5	41.7%
4	1	8.3%
1	1	8.3%

Dash Punctuation

Value	Count	Frequency (%)
-	30	85.7%
–	5	14.3%

Space Separator

Value	Count	Frequency (%)
	564	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	11	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	11	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	5074	88.0%
Common	691	12.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	647	12.8%
a	633	12.5%
i	530	10.4%
e	485	9.6%
n	474	9.3%
s	276	5.4%
c	254	5.0%
S	229	4.5%
r	225	4.4%
D	206	4.1%
Other values (38)	1115	22.0%

Common

Value	Count	Frequency (%)
	564	81.6%
-	30	4.3%
,	25	3.6%
/	17	2.5%
(	11	1.6%
)	11	1.6%
.	8	1.2%
&	7	1.0%
2	5	0.7%
0	5	0.7%
Other values (4)	8	1.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	5760	99.9%
Punctuation	5	0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	647	11.2%
a	633	11.0%
	564	9.8%
i	530	9.2%
e	485	8.4%
n	474	8.2%
s	276	4.8%
c	254	4.4%
S	229	4.0%
r	225	3.9%
Other values (51)	1443	25.1%

Punctuation

Value	Count	Frequency (%)
–	5	100.0%

Rating
Real number (ℝ)

HIGH CORRELATION

Distinct	30
Distinct (%)	12.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.714529915

Minimum	-1
Maximum	5
Zeros	0
Zeros (%)	0.0%
Negative	1
Negative (%)	0.4%
Memory size	2.0 KiB

Quantile statistics

Minimum	-1
5-th percentile	2.7
Q1	3.4
median	3.8
Q3	4.1
95-th percentile	4.7
Maximum	5
Range	6
Interquartile range (IQR)	0.7

Descriptive statistics

Standard deviation	0.6714617911
Coefficient of variation (CV)	0.1807662898
Kurtosis	9.790065755
Mean	3.714529915
Median Absolute Deviation (MAD)	0.4
Skewness	-1.760643289
Sum	869.2
Variance	0.4508609369
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=30)

Value	Count	Frequency (%)
3.8	29	12.4%
3.9	23	9.8%
3.4	18	7.7%
3.5	17	7.3%
4	17	7.3%
4.7	14	6.0%
3.2	10	4.3%
3.3	10	4.3%
4.3	10	4.3%
3.7	10	4.3%
Other values (20)	76	32.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
-1	1	0.4%
1.9	2	0.9%
2.1	2	0.9%
2.2	1	0.4%
2.3	2	0.9%
2.4	2	0.9%
2.5	1	0.4%
2.7	5	2.1%
2.8	4	1.7%
2.9	2	0.9%

Value	Count	Frequency (%)
5	1	0.4%
4.8	3	1.3%
4.7	14	6.0%
4.6	7	3.0%
4.5	4	1.7%
4.4	8	3.4%
4.3	10	4.3%
4.2	6	2.6%
4.1	7	3.0%
4	17	7.3%

Size
Categorical

HIGH CORRELATION

Distinct	7
Distinct (%)	3.0%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

1001 - 5000	55
201 - 500	44
501 - 1000	40
51 - 200	38
10000+	29
Other values (2)	28

Length

Max length	13
Median length	11
Mean length	10.18376068
Min length	7

Characters and Unicode

Total characters	2383
Distinct characters	7
Distinct categories	4 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	501 - 1000
2nd row	10000+
3rd row	501 - 1000
4th row	1001 - 5000
5th row	201 - 500

Common Values

Value	Count	Frequency (%)
1001 - 5000	55	23.5%
201 - 500	44	18.8%
501 - 1000	40	17.1%
51 - 200	38	16.2%
10000+	29	12.4%
5001 - 10000	17	7.3%
1 - 50	11	4.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
	205	31.8%
1001	55	8.5%
5000	55	8.5%
10000	46	7.1%
201	44	6.8%
500	44	6.8%
501	40	6.2%
1000	40	6.2%
51	38	5.9%
200	38	5.9%
Other values (3)	39	6.1%

Most occurring characters

Value	Count	Frequency (%)
0	872	36.6%
	644	27.0%
1	346	14.5%
-	205	8.6%
5	205	8.6%
2	82	3.4%
+	29	1.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1505	63.2%
Space Separator	644	27.0%
Dash Punctuation	205	8.6%
Math Symbol	29	1.2%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	872	57.9%
1	346	23.0%
5	205	13.6%
2	82	5.4%

Space Separator

Value	Count	Frequency (%)
	644	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	205	100.0%

Math Symbol

Value	Count	Frequency (%)
+	29	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	2383	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	872	36.6%
	644	27.0%
1	346	14.5%
-	205	8.6%
5	205	8.6%
2	82	3.4%
+	29	1.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2383	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	872	36.6%
	644	27.0%
1	346	14.5%
-	205	8.6%
5	205	8.6%
2	82	3.4%
+	29	1.2%

Founded
Real number (ℝ)

HIGH CORRELATION

Distinct	54
Distinct (%)	23.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1840.799145

Minimum	-1
Maximum	2019
Zeros	0
Zeros (%)	0.0%
Negative	18
Negative (%)	7.7%
Memory size	2.0 KiB

Quantile statistics

Minimum	-1
5-th percentile	-1
Q1	1977
median	1996
Q3	2006
95-th percentile	2014
Maximum	2019
Range	2020
Interquartile range (IQR)	29

Descriptive statistics

Standard deviation	533.032178
Coefficient of variation (CV)	0.2895656375
Kurtosis	8.267319491
Mean	1840.799145
Median Absolute Deviation (MAD)	12
Skewness	-3.191483543
Sum	430747
Variance	284123.3028
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
-1	18	7.7%
1996	15	6.4%
2010	13	5.6%
2006	13	5.6%
2008	12	5.1%
2002	9	3.8%
1997	7	3.0%
1965	7	3.0%
2000	7	3.0%
1982	6	2.6%
Other values (44)	127	54.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
-1	18	7.7%
1961	4	1.7%
1962	1	0.4%
1965	7	3.0%
1966	3	1.3%
1967	2	0.9%
1968	5	2.1%
1969	5	2.1%
1970	2	0.9%
1971	1	0.4%

Value	Count	Frequency (%)
2019	1	0.4%
2017	4	1.7%
2016	2	0.9%
2015	1	0.4%
2014	5	2.1%
2013	3	1.3%
2012	5	2.1%
2011	5	2.1%
2010	13	5.6%
2009	3	1.3%

Type of ownership
Categorical

HIGH CORRELATION

Distinct	8
Distinct (%)	3.4%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

Company - Private	134
Company - Public	62
Subsidiary or Business Segment	15
Government	7
Nonprofit Organization	6
Other values (3)	10

Length

Max length	30
Median length	17
Mean length	17.36752137
Min length	8

Characters and Unicode

Total characters	4064
Distinct characters	33
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Company - Private
2nd row	Other Organization
3rd row	Company - Private
4th row	Government
5th row	Company - Public

Common Values

Value	Count	Frequency (%)
Company - Private	134	57.3%
Company - Public	62	26.5%
Subsidiary or Business Segment	15	6.4%
Government	7	3.0%
Nonprofit Organization	6	2.6%
Hospital	5	2.1%
Other Organization	3	1.3%
School / School District	2	0.9%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
	198	28.9%
company	196	28.6%
private	134	19.5%
public	62	9.0%
subsidiary	15	2.2%
or	15	2.2%
business	15	2.2%
segment	15	2.2%
organization	9	1.3%
government	7	1.0%
Other values (5)	20	2.9%

Most occurring characters

Value	Count	Frequency (%)
	452	11.1%
a	368	9.1%
i	274	6.7%
n	264	6.5%
o	252	6.2%
m	218	5.4%
y	211	5.2%
p	207	5.1%
C	196	4.8%
-	196	4.8%
Other values (23)	1426	35.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2941	72.4%
Uppercase Letter	473	11.6%
Space Separator	452	11.1%
Dash Punctuation	196	4.8%
Other Punctuation	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	368	12.5%
i	274	9.3%
n	264	9.0%
o	252	8.6%
m	218	7.4%
y	211	7.2%
p	207	7.0%
e	196	6.7%
r	191	6.5%
t	183	6.2%
Other values (11)	577	19.6%

Uppercase Letter

Value	Count	Frequency (%)
C	196	41.4%
P	196	41.4%
S	34	7.2%
B	15	3.2%
O	12	2.5%
G	7	1.5%
N	6	1.3%
H	5	1.1%
D	2	0.4%

Space Separator

Value	Count	Frequency (%)
	452	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	196	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	3414	84.0%
Common	650	16.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	368	10.8%
i	274	8.0%
n	264	7.7%
o	252	7.4%
m	218	6.4%
y	211	6.2%
p	207	6.1%
C	196	5.7%
P	196	5.7%
e	196	5.7%
Other values (20)	1032	30.2%

Common

Value	Count	Frequency (%)
	452	69.5%
-	196	30.2%
/	2	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4064	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	452	11.1%
a	368	9.1%
i	274	6.7%
n	264	6.5%
o	252	6.2%
m	218	5.4%
y	211	5.2%
p	207	5.1%
C	196	4.8%
-	196	4.8%
Other values (23)	1426	35.1%

Industry
Categorical

HIGH CORRELATION

Distinct	43
Distinct (%)	18.4%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

Biotech & Pharmaceuticals	29
IT Services	24
Computer Hardware & Software	23
Consulting	15
Enterprise Software & Network Solutions	13
Other values (38)	130

Length

Max length	39
Median length	32
Mean length	20.81623932
Min length	2

Characters and Unicode

Total characters	4871
Distinct characters	51
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	17 ?
Unique (%)	7.3%

Sample

1st row	Aerospace & Defense
2nd row	Health Care Services & Hospitals
3rd row	Security Services
4th row	Energy
5th row	Real Estate

Common Values

Value	Count	Frequency (%)
Biotech & Pharmaceuticals	29	12.4%
IT Services	24	10.3%
Computer Hardware & Software	23	9.8%
Consulting	15	6.4%
Enterprise Software & Network Solutions	13	5.6%
Aerospace & Defense	12	5.1%
Insurance Carriers	12	5.1%
Health Care Services & Hospitals	11	4.7%
Internet	10	4.3%
Energy	8	3.4%
Other values (33)	77	32.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	124	19.1%
services	43	6.6%
software	36	5.6%
biotech	29	4.5%
pharmaceuticals	29	4.5%
it	24	3.7%
computer	23	3.5%
hardware	23	3.5%
consulting	15	2.3%
enterprise	13	2.0%
Other values (70)	289	44.6%

Most occurring characters

Value	Count	Frequency (%)
e	558	11.5%
	414	8.5%
r	392	8.0%
a	350	7.2%
t	323	6.6%
i	276	5.7%
s	266	5.5%
n	253	5.2%
c	227	4.7%
o	226	4.6%
Other values (41)	1586	32.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	3769	77.4%
Uppercase Letter	548	11.3%
Space Separator	414	8.5%
Other Punctuation	132	2.7%
Decimal Number	5	0.1%
Dash Punctuation	3	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	558	14.8%
r	392	10.4%
a	350	9.3%
t	323	8.6%
i	276	7.3%
s	266	7.1%
n	253	6.7%
c	227	6.0%
o	226	6.0%
u	142	3.8%
Other values (14)	756	20.1%

Uppercase Letter

Value	Count	Frequency (%)
S	111	20.3%
C	70	12.8%
I	48	8.8%
H	46	8.4%
T	36	6.6%
A	35	6.4%
P	35	6.4%
B	34	6.2%
E	31	5.7%
D	23	4.2%
Other values (11)	79	14.4%

Other Punctuation

Value	Count	Frequency (%)
&	124	93.9%
,	8	6.1%

Decimal Number

Value	Count	Frequency (%)
1	3	60.0%
2	2	40.0%

Space Separator

Value	Count	Frequency (%)
	414	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	3	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	4317	88.6%
Common	554	11.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	558	12.9%
r	392	9.1%
a	350	8.1%
t	323	7.5%
i	276	6.4%
s	266	6.2%
n	253	5.9%
c	227	5.3%
o	226	5.2%
u	142	3.3%
Other values (35)	1304	30.2%

Common

Value	Count	Frequency (%)
	414	74.7%
&	124	22.4%
,	8	1.4%
-	3	0.5%
1	3	0.5%
2	2	0.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4871	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	558	11.5%
	414	8.5%
r	392	8.0%
a	350	7.2%
t	323	6.6%
i	276	5.7%
s	266	5.5%
n	253	5.2%
c	227	4.7%
o	226	4.6%
Other values (41)	1586	32.6%

Sector
Categorical

HIGH CORRELATION

Distinct	22
Distinct (%)	9.4%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

Information Technology	70
Business Services	43
Biotech & Pharmaceuticals	29
Finance	13
Aerospace & Defense	12
Other values (17)	67

Length

Max length	32
Median length	26
Mean length	18
Min length	2

Characters and Unicode

Total characters	4212
Distinct characters	42
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	6 ?
Unique (%)	2.6%

Sample

1st row	Aerospace & Defense
2nd row	Health Care
3rd row	Business Services
4th row	Oil, Gas, Energy & Utilities
5th row	Real Estate

Common Values

Value	Count	Frequency (%)
Information Technology	70	29.9%
Business Services	43	18.4%
Biotech & Pharmaceuticals	29	12.4%
Finance	13	5.6%
Aerospace & Defense	12	5.1%
Insurance	12	5.1%
Health Care	11	4.7%
Oil, Gas, Energy & Utilities	8	3.4%
Manufacturing	7	3.0%
Retail	4	1.7%
Other values (12)	25	10.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
information	70	14.2%
technology	70	14.2%
	56	11.4%
services	44	8.9%
business	43	8.7%
biotech	29	5.9%
pharmaceuticals	29	5.9%
finance	13	2.6%
defense	12	2.4%
insurance	12	2.4%
Other values (27)	114	23.2%

Most occurring characters

Value	Count	Frequency (%)
e	415	9.9%
n	359	8.5%
o	345	8.2%
i	295	7.0%
s	270	6.4%
a	268	6.4%
	258	6.1%
c	258	6.1%
r	210	5.0%
t	192	4.6%
Other values (32)	1342	31.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	3440	81.7%
Uppercase Letter	437	10.4%
Space Separator	258	6.1%
Other Punctuation	73	1.7%
Dash Punctuation	3	0.1%
Decimal Number	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	415	12.1%
n	359	10.4%
o	345	10.0%
i	295	8.6%
s	270	7.8%
a	268	7.8%
c	258	7.5%
r	210	6.1%
t	192	5.6%
l	142	4.1%
Other values (9)	686	19.9%

Uppercase Letter

Value	Count	Frequency (%)
I	82	18.8%
T	82	18.8%
B	72	16.5%
S	44	10.1%
P	31	7.1%
E	16	3.7%
A	14	3.2%
F	13	3.0%
D	12	2.7%
C	12	2.7%
Other values (8)	59	13.5%

Other Punctuation

Value	Count	Frequency (%)
&	56	76.7%
,	17	23.3%

Space Separator

Value	Count	Frequency (%)
	258	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	3	100.0%

Decimal Number

Value	Count	Frequency (%)
1	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	3877	92.0%
Common	335	8.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	415	10.7%
n	359	9.3%
o	345	8.9%
i	295	7.6%
s	270	7.0%
a	268	6.9%
c	258	6.7%
r	210	5.4%
t	192	5.0%
l	142	3.7%
Other values (27)	1123	29.0%

Common

Value	Count	Frequency (%)
	258	77.0%
&	56	16.7%
,	17	5.1%
-	3	0.9%
1	1	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4212	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	415	9.9%
n	359	8.5%
o	345	8.2%
i	295	7.0%
s	270	6.4%
a	268	6.4%
	258	6.1%
c	258	6.1%
r	210	5.0%
t	192	4.6%
Other values (32)	1342	31.9%

Hourly
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	229
1	5

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Most occurring characters

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Employer provided
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	233
1	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.4%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	233	99.6%
1	1	0.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	233	99.6%
1	1	0.4%

Most occurring characters

Value	Count	Frequency (%)
0	233	99.6%
1	1	0.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	233	99.6%
1	1	0.4%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	233	99.6%
1	1	0.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	233	99.6%
1	1	0.4%

Lower Salary
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	90
Distinct (%)	38.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	71.76068376

Minimum	15
Maximum	200
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	15
5-th percentile	32.65
Q1	52
median	68
Q3	86
95-th percentile	118.7
Maximum	200
Range	185
Interquartile range (IQR)	34

Descriptive statistics

Standard deviation	28.24893923
Coefficient of variation (CV)	0.3936548226
Kurtosis	2.04284193
Mean	71.76068376
Median Absolute Deviation (MAD)	18
Skewness	0.9554970909
Sum	16792
Variance	798.0025678
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
52	7	3.0%
64	6	2.6%
83	6	2.6%
60	6	2.6%
74	6	2.6%
80	6	2.6%
86	5	2.1%
55	5	2.1%
65	5	2.1%
42	5	2.1%
Other values (80)	177	75.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
15	1	0.4%
20	2	0.9%
27	1	0.4%
29	1	0.4%
31	4	1.7%
32	3	1.3%
33	2	0.9%
34	1	0.4%
35	1	0.4%
36	1	0.4%

Value	Count	Frequency (%)
200	1	0.4%
190	1	0.4%
139	1	0.4%
138	1	0.4%
132	1	0.4%
131	1	0.4%
130	1	0.4%
127	1	0.4%
126	2	0.9%
121	1	0.4%

Upper Salary
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	122
Distinct (%)	52.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	124.5512821

Minimum	16
Maximum	250
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	16
5-th percentile	61.65
Q1	95.25
median	120.5
Q3	148
95-th percentile	207.35
Maximum	250
Range	234
Interquartile range (IQR)	52.75

Descriptive statistics

Standard deviation	42.73161501
Coefficient of variation (CV)	0.3430845055
Kurtosis	0.05296412477
Mean	124.5512821
Median Absolute Deviation (MAD)	27
Skewness	0.428129645
Sum	29145
Variance	1825.990921
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
113	7	3.0%
133	5	2.1%
85	5	2.1%
137	5	2.1%
127	5	2.1%
119	5	2.1%
142	4	1.7%
126	4	1.7%
140	4	1.7%
112	4	1.7%
Other values (112)	186	79.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
16	1	0.4%
35	1	0.4%
39	1	0.4%
48	1	0.4%
50	1	0.4%
52	1	0.4%
55	1	0.4%
57	2	0.9%
59	1	0.4%
60	1	0.4%

Value	Count	Frequency (%)
250	1	0.4%
231	1	0.4%
228	2	0.9%
224	1	0.4%
223	1	0.4%
221	1	0.4%
220	2	0.9%
211	1	0.4%
209	1	0.4%
208	1	0.4%

Avg Salary(K)
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	153
Distinct (%)	65.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	98.15598291

Minimum	15.5
Maximum	225
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	15.5
5-th percentile	47.325
Q1	72.875
median	95
Q3	117.375
95-th percentile	164.9
Maximum	225
Range	209.5
Interquartile range (IQR)	44.5

Descriptive statistics

Standard deviation	35.0578649
Coefficient of variation (CV)	0.3571648295
Kurtosis	0.3956477174
Mean	98.15598291
Median Absolute Deviation (MAD)	22.5
Skewness	0.5724370744
Sum	22968.5
Variance	1229.053891
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
87.5	5	2.1%
121	4	1.7%
76.5	4	1.7%
98.5	4	1.7%
87	4	1.7%
107	4	1.7%
100	3	1.3%
80.5	3	1.3%
56.5	3	1.3%
81	3	1.3%
Other values (143)	197	84.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
15.5	1	0.4%
27.5	1	0.4%
29.5	1	0.4%
37.5	1	0.4%
39.5	1	0.4%
43	1	0.4%
44	1	0.4%
44.5	2	0.9%
45.5	1	0.4%
47	2	0.9%

Value	Count	Frequency (%)
225	1	0.4%
205	1	0.4%
181	1	0.4%
180	1	0.4%
177	1	0.4%
174	1	0.4%
173	1	0.4%
171.5	1	0.4%
169	1	0.4%
168	2	0.9%

Job Location
Categorical

HIGH CORRELATION

Distinct	32
Distinct (%)	13.7%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

CA	58
NY	23
MA	23
VA	21
IL	13
Other values (27)	96

Length

Max length	2
Median length	2
Mean length	2
Min length	2

Characters and Unicode

Total characters	468
Distinct characters	24
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	7 ?
Unique (%)	3.0%

Sample

1st row	NM
2nd row	MD
3rd row	FL
4th row	WA
5th row	TX

Common Values

Value	Count	Frequency (%)
CA	58	24.8%
NY	23	9.8%
MA	23	9.8%
VA	21	9.0%
IL	13	5.6%
MD	12	5.1%
PA	10	4.3%
WA	9	3.8%
TX	8	3.4%
FL	6	2.6%
Other values (22)	51	21.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
ca	58	24.8%
ny	23	9.8%
ma	23	9.8%
va	21	9.0%
il	13	5.6%
md	12	5.1%
pa	10	4.3%
wa	9	3.8%
tx	8	3.4%
fl	6	2.6%
Other values (22)	51	21.8%

Most occurring characters

Value	Count	Frequency (%)
A	133	28.4%
C	69	14.7%
M	42	9.0%
N	38	8.1%
Y	23	4.9%
L	23	4.9%
I	22	4.7%
V	21	4.5%
D	18	3.8%
T	14	3.0%
Other values (14)	65	13.9%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	468	100.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
A	133	28.4%
C	69	14.7%
M	42	9.0%
N	38	8.1%
Y	23	4.9%
L	23	4.9%
I	22	4.7%
V	21	4.5%
D	18	3.8%
T	14	3.0%
Other values (14)	65	13.9%

Most occurring scripts

Value	Count	Frequency (%)
Latin	468	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
A	133	28.4%
C	69	14.7%
M	42	9.0%
N	38	8.1%
Y	23	4.9%
L	23	4.9%
I	22	4.7%
V	21	4.5%
D	18	3.8%
T	14	3.0%
Other values (14)	65	13.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	468	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
A	133	28.4%
C	69	14.7%
M	42	9.0%
N	38	8.1%
Y	23	4.9%
L	23	4.9%
I	22	4.7%
V	21	4.5%
D	18	3.8%
T	14	3.0%
Other values (14)	65	13.9%

Age
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	53
Distinct (%)	22.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	26.27777778

Minimum	2
Maximum	60
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	2
5-th percentile	7
Q1	15
median	22
Q3	36.75
95-th percentile	55.35
Maximum	60
Range	58
Interquartile range (IQR)	21.75

Descriptive statistics

Standard deviation	15.06822032
Coefficient of variation (CV)	0.5734206466
Kurtosis	-0.5800323981
Mean	26.27777778
Median Absolute Deviation (MAD)	9
Skewness	0.6866389576
Sum	6149
Variance	227.0512637
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
21	25	10.7%
25	15	6.4%
11	13	5.6%
15	13	5.6%
13	12	5.1%
19	9	3.8%
56	7	3.0%
24	7	3.0%
39	6	2.6%
22	6	2.6%
Other values (43)	121	51.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
2	1	0.4%
4	4	1.7%
5	2	0.9%
6	1	0.4%
7	5	2.1%
8	3	1.3%
9	5	2.1%
10	5	2.1%
11	13	5.6%
12	3	1.3%

Value	Count	Frequency (%)
60	4	1.7%
59	1	0.4%
56	7	3.0%
55	3	1.3%
54	2	0.9%
53	5	2.1%
52	5	2.1%
51	2	0.9%
50	1	0.4%
49	2	0.9%

Python
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

1	130
0	104

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	130	55.6%
0	104	44.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
1	130	55.6%
0	104	44.4%

Most occurring characters

Value	Count	Frequency (%)
1	130	55.6%
0	104	44.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	130	55.6%
0	104	44.4%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	130	55.6%
0	104	44.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	130	55.6%
0	104	44.4%

spark
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	176
1	58

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	1
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	176	75.2%
1	58	24.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	176	75.2%
1	58	24.8%

Most occurring characters

Value	Count	Frequency (%)
0	176	75.2%
1	58	24.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	176	75.2%
1	58	24.8%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	176	75.2%
1	58	24.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	176	75.2%
1	58	24.8%

aws
Categorical

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	180
1	54

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	180	76.9%
1	54	23.1%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	180	76.9%
1	54	23.1%

Most occurring characters

Value	Count	Frequency (%)
0	180	76.9%
1	54	23.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	180	76.9%
1	54	23.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	180	76.9%
1	54	23.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	180	76.9%
1	54	23.1%

excel
Categorical

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

1	120
0	114

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	1
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
1	120	51.3%
0	114	48.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
1	120	51.3%
0	114	48.7%

Most occurring characters

Value	Count	Frequency (%)
1	120	51.3%
0	114	48.7%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	120	51.3%
0	114	48.7%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	120	51.3%
0	114	48.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	120	51.3%
0	114	48.7%

sql
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

1	139
0	95

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	1
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
1	139	59.4%
0	95	40.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
1	139	59.4%
0	95	40.6%

Most occurring characters

Value	Count	Frequency (%)
1	139	59.4%
0	95	40.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	139	59.4%
0	95	40.6%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	139	59.4%
0	95	40.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	139	59.4%
0	95	40.6%

sas
Categorical

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	210
1	24

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	1
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

Most occurring characters

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

keras
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	226
1	8

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	226	96.6%
1	8	3.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	226	96.6%
1	8	3.4%

Most occurring characters

Value	Count	Frequency (%)
0	226	96.6%
1	8	3.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	226	96.6%
1	8	3.4%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	226	96.6%
1	8	3.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	226	96.6%
1	8	3.4%

pytorch
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	220
1	14

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	220	94.0%
1	14	6.0%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	220	94.0%
1	14	6.0%

Most occurring characters

Value	Count	Frequency (%)
0	220	94.0%
1	14	6.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	220	94.0%
1	14	6.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	220	94.0%
1	14	6.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	220	94.0%
1	14	6.0%

scikit
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	214
1	20

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	214	91.5%
1	20	8.5%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	214	91.5%
1	20	8.5%

Most occurring characters

Value	Count	Frequency (%)
0	214	91.5%
1	20	8.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	214	91.5%
1	20	8.5%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	214	91.5%
1	20	8.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	214	91.5%
1	20	8.5%

tensor
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	210
1	24

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

Most occurring characters

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	210	89.7%
1	24	10.3%

hadoop
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	187
1	47

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	187	79.9%
1	47	20.1%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	187	79.9%
1	47	20.1%

Most occurring characters

Value	Count	Frequency (%)
0	187	79.9%
1	47	20.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	187	79.9%
1	47	20.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	187	79.9%
1	47	20.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	187	79.9%
1	47	20.1%

tableau
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	183
1	51

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	183	78.2%
1	51	21.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	183	78.2%
1	51	21.8%

Most occurring characters

Value	Count	Frequency (%)
0	183	78.2%
1	51	21.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	183	78.2%
1	51	21.8%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	183	78.2%
1	51	21.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	183	78.2%
1	51	21.8%

bi
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	213
1	21

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	213	91.0%
1	21	9.0%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	213	91.0%
1	21	9.0%

Most occurring characters

Value	Count	Frequency (%)
0	213	91.0%
1	21	9.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	213	91.0%
1	21	9.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	213	91.0%
1	21	9.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	213	91.0%
1	21	9.0%

flink
Categorical

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	229
1	5

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Most occurring characters

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	229	97.9%
1	5	2.1%

mongo
Categorical

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	221
1	13

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	221	94.4%
1	13	5.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	221	94.4%
1	13	5.6%

Most occurring characters

Value	Count	Frequency (%)
0	221	94.4%
1	13	5.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	221	94.4%
1	13	5.6%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	221	94.4%
1	13	5.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	221	94.4%
1	13	5.6%

google_an
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.9%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

0	227
1	7

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	234
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	227	97.0%
1	7	3.0%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	227	97.0%
1	7	3.0%

Most occurring characters

Value	Count	Frequency (%)
0	227	97.0%
1	7	3.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	234	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	227	97.0%
1	7	3.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	234	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	227	97.0%
1	7	3.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	234	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	227	97.0%
1	7	3.0%

job_title_sim
Categorical

HIGH CORRELATION

Distinct	9
Distinct (%)	3.8%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

data scientist	103
analyst	42
data engineer	41
other scientist	33
Data scientist project manager	4
Other values (4)	11

Length

Max length	30
Median length	25
Mean length	12.90598291
Min length	2

Characters and Unicode

Total characters	3020
Distinct characters	19
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	data scientist
2nd row	data scientist
3rd row	data scientist
4th row	data scientist
5th row	data scientist

Common Values

Value	Count	Frequency (%)
data scientist	103	44.0%
analyst	42	17.9%
data engineer	41	17.5%
other scientist	33	14.1%
Data scientist project manager	4	1.7%
data analitics	4	1.7%
na	3	1.3%
data modeler	2	0.9%
machine learning engineer	2	0.9%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
data	154	35.6%
scientist	140	32.3%
engineer	43	9.9%
analyst	42	9.7%
other	33	7.6%
project	4	0.9%
manager	4	0.9%
analitics	4	0.9%
na	3	0.7%
modeler	2	0.5%
Other values (2)	4	0.9%

Most occurring characters

Value	Count	Frequency (%)
t	517	17.1%
a	415	13.7%
i	335	11.1%
s	326	10.8%
e	318	10.5%
n	285	9.4%
	199	6.6%
d	152	5.0%
c	150	5.0%
r	88	2.9%
Other values (9)	235	7.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2817	93.3%
Space Separator	199	6.6%
Uppercase Letter	4	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	517	18.4%
a	415	14.7%
i	335	11.9%
s	326	11.6%
e	318	11.3%
n	285	10.1%
d	152	5.4%
c	150	5.3%
r	88	3.1%
l	50	1.8%
Other values (7)	181	6.4%

Space Separator

Value	Count	Frequency (%)
	199	100.0%

Uppercase Letter

Value	Count	Frequency (%)
D	4	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2821	93.4%
Common	199	6.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	517	18.3%
a	415	14.7%
i	335	11.9%
s	326	11.6%
e	318	11.3%
n	285	10.1%
d	152	5.4%
c	150	5.3%
r	88	3.1%
l	50	1.8%
Other values (8)	185	6.6%

Common

Value	Count	Frequency (%)
	199	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3020	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	517	17.1%
a	415	13.7%
i	335	11.1%
s	326	10.8%
e	318	10.5%
n	285	9.4%
	199	6.6%
d	152	5.0%
c	150	5.0%
r	88	2.9%
Other values (9)	235	7.8%

seniority_by_title
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

na	170
sr	63
jr	1

Length

Max length	2
Median length	2
Mean length	2
Min length	2

Characters and Unicode

Total characters	468
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.4%

Sample

1st row	na
2nd row	na
3rd row	na
4th row	na
5th row	na

Common Values

Value	Count	Frequency (%)
na	170	72.6%
sr	63	26.9%
jr	1	0.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
na	170	72.6%
sr	63	26.9%
jr	1	0.4%

Most occurring characters

Value	Count	Frequency (%)
n	170	36.3%
a	170	36.3%
r	64	13.7%
s	63	13.5%
j	1	0.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	468	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	170	36.3%
a	170	36.3%
r	64	13.7%
s	63	13.5%
j	1	0.2%

Most occurring scripts

Value	Count	Frequency (%)
Latin	468	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	170	36.3%
a	170	36.3%
r	64	13.7%
s	63	13.5%
j	1	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	468	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	170	36.3%
a	170	36.3%
r	64	13.7%
s	63	13.5%
j	1	0.2%

Degree
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

na	131
M	76
P	27

Length

Max length	2
Median length	2
Mean length	1.55982906
Min length	1

Characters and Unicode

Total characters	365
Distinct characters	4
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	M
2nd row	M
3rd row	M
4th row	na
5th row	na

Common Values

Value	Count	Frequency (%)
na	131	56.0%
M	76	32.5%
P	27	11.5%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
na	131	56.0%
m	76	32.5%
p	27	11.5%

Most occurring characters

Value	Count	Frequency (%)
n	131	35.9%
a	131	35.9%
M	76	20.8%
P	27	7.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	262	71.8%
Uppercase Letter	103	28.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	131	50.0%
a	131	50.0%

Uppercase Letter

Value	Count	Frequency (%)
M	76	73.8%
P	27	26.2%

Most occurring scripts

Value	Count	Frequency (%)
Latin	365	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	131	35.9%
a	131	35.9%
M	76	20.8%
P	27	7.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	365	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	131	35.9%
a	131	35.9%
M	76	20.8%
P	27	7.4%

Min_Salary
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	94
Distinct (%)	40.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	71.31196581

Minimum	10
Maximum	200
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	10
5-th percentile	31
Q1	51.25
median	68
Q3	86
95-th percentile	118.7
Maximum	200
Range	190
Interquartile range (IQR)	34.75

Descriptive statistics

Standard deviation	28.85305575
Coefficient of variation (CV)	0.4046032867
Kurtosis	1.867899292
Mean	71.31196581
Median Absolute Deviation (MAD)	18
Skewness	0.8536435912
Sum	16687
Variance	832.4988262
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
52	7	3.0%
83	6	2.6%
80	6	2.6%
60	6	2.6%
64	6	2.6%
74	6	2.6%
86	5	2.1%
65	5	2.1%
63	5	2.1%
42	5	2.1%
Other values (84)	177	75.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
10	1	0.4%
15	1	0.4%
18	1	0.4%
20	1	0.4%
21	1	0.4%
24	1	0.4%
27	2	0.9%
29	1	0.4%
31	4	1.7%
32	3	1.3%

Value	Count	Frequency (%)
200	1	0.4%
190	1	0.4%
139	1	0.4%
138	1	0.4%
132	1	0.4%
131	1	0.4%
130	1	0.4%
127	1	0.4%
126	2	0.9%
121	1	0.4%

Max_Salary
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	123
Distinct (%)	52.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	123.8333333

Minimum	16
Maximum	250
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	16
5-th percentile	58.3
Q1	95
median	120.5
Q3	148
95-th percentile	207.35
Maximum	250
Range	234
Interquartile range (IQR)	53

Descriptive statistics

Standard deviation	43.90638207
Coefficient of variation (CV)	0.3545602859
Kurtosis	0.09923200825
Mean	123.8333333
Median Absolute Deviation (MAD)	27.5
Skewness	0.3048904112
Sum	28977
Variance	1927.770386
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
113	7	3.0%
119	5	2.1%
133	5	2.1%
85	5	2.1%
127	5	2.1%
137	5	2.1%
112	4	1.7%
129	4	1.7%
140	4	1.7%
142	4	1.7%
Other values (113)	186	79.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
16	1	0.4%
17	1	0.4%
25	1	0.4%
29	1	0.4%
39	2	0.9%
47	1	0.4%
48	1	0.4%
50	1	0.4%
55	1	0.4%
57	2	0.9%

Value	Count	Frequency (%)
250	1	0.4%
231	1	0.4%
228	2	0.9%
224	1	0.4%
223	1	0.4%
221	1	0.4%
220	2	0.9%
211	1	0.4%
209	1	0.4%
208	1	0.4%

Min_Revenue
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	10
Distinct (%)	4.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1337752137

Minimum	1000000
Maximum	1 × 10¹⁰
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	1000000
5-th percentile	10000000
Q1	50000000
median	100000000
Q3	1000000000
95-th percentile	1 × 10¹⁰
Maximum	1 × 10¹⁰
Range	9999000000
Interquartile range (IQR)	950000000

Descriptive statistics

Standard deviation	2798745298
Coefficient of variation (CV)	2.092125455
Kurtosis	5.135877727
Mean	1337752137
Median Absolute Deviation (MAD)	90000000
Skewness	2.555829799
Sum	3.13034 × 10¹¹
Variance	7.832975243 × 10¹⁸
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
100000000	48	20.5%
50000000	40	17.1%
500000000	25	10.7%
1000000000	24	10.3%
2000000000	22	9.4%
25000000	21	9.0%
1 × 10¹⁰	20	8.5%
10000000	20	8.5%
1000000	9	3.8%
5000000000	5	2.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1000000	9	3.8%
10000000	20	8.5%
25000000	21	9.0%
50000000	40	17.1%
100000000	48	20.5%
500000000	25	10.7%
1000000000	24	10.3%
2000000000	22	9.4%
5000000000	5	2.1%
1 × 10¹⁰	20	8.5%

Value	Count	Frequency (%)
1 × 10¹⁰	20	8.5%
5000000000	5	2.1%
2000000000	22	9.4%
1000000000	24	10.3%
500000000	25	10.7%
100000000	48	20.5%
50000000	40	17.1%
25000000	21	9.0%
10000000	20	8.5%
1000000	9	3.8%

Max_Revenue
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	11
Distinct (%)	4.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1971833333

Minimum	1000000
Maximum	1 × 10¹⁰
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	1000000
5-th percentile	10000000
Q1	50000000
median	500000000
Q3	2000000000
95-th percentile	1 × 10¹⁰
Maximum	1 × 10¹⁰
Range	9999000000
Interquartile range (IQR)	1950000000

Descriptive statistics

Standard deviation	3123711602
Coefficient of variation (CV)	1.584166141
Kurtosis	1.918435744
Mean	1971833333
Median Absolute Deviation (MAD)	490000000
Skewness	1.807959976
Sum	4.61409 × 10¹¹
Variance	9.757574174 × 10¹⁸
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
500000000	48	20.5%
100000000	27	11.5%
1000000000	25	10.7%
1 × 10¹⁰	25	10.7%
2000000000	24	10.3%
5000000000	22	9.4%
50000000	21	9.0%
25000000	20	8.5%
10000000	13	5.6%
5000000	5	2.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1000000	4	1.7%
5000000	5	2.1%
10000000	13	5.6%
25000000	20	8.5%
50000000	21	9.0%
100000000	27	11.5%
500000000	48	20.5%
1000000000	25	10.7%
2000000000	24	10.3%
5000000000	22	9.4%

Value	Count	Frequency (%)
1 × 10¹⁰	25	10.7%
5000000000	22	9.4%
2000000000	24	10.3%
1000000000	25	10.7%
500000000	48	20.5%
100000000	27	11.5%
50000000	21	9.0%
25000000	20	8.5%
10000000	13	5.6%
5000000	5	2.1%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	Job Title	Rating	Size	Founded	Type of ownership	Industry	Sector	Lower Salary	Upper Salary	Avg Salary(K)	Job Location	Age	Python	spark	aws	excel	sql	sas	pytorch	tensor	hadoop	tableau	bi	mongo	job_title_sim	seniority_by_title	Degree	Min_Salary	Max_Salary	Min_Revenue	Max_Revenue
0	Data Scientist	3.8	501 - 1000	1973	Company - Private	Aerospace & Defense	Aerospace & Defense	53	91	72.0	NM	48	1	0	0	1	0	1	0	0	0	1	1	0	data scientist	na	M	53	91	50000000	100000000
1	Healthcare Data Scientist	3.4	10000+	1984	Other Organization	Health Care Services & Hospitals	Health Care	63	112	87.5	MD	37	1	0	0	0	0	0	0	0	0	0	0	0	data scientist	na	M	63	112	2000000000	5000000000
2	Data Scientist	4.8	501 - 1000	2010	Company - Private	Security Services	Business Services	80	90	85.0	FL	11	1	1	0	1	1	1	0	0	0	0	0	0	data scientist	na	M	80	90	100000000	500000000
3	Data Scientist	3.8	1001 - 5000	1965	Government	Energy	Oil, Gas, Energy & Utilities	56	97	76.5	WA	56	1	0	0	0	0	0	0	0	0	0	0	0	data scientist	na	na	56	97	500000000	1000000000
4	Data Scientist	3.4	201 - 500	2000	Company - Public	Real Estate	Real Estate	71	119	95.0	TX	21	1	0	1	1	1	0	0	0	0	0	1	1	data scientist	na	na	71	119	1000000000	2000000000
5	Data Scientist	3.8	201 - 500	2005	Company - Private	Consulting	Business Services	86	142	114.0	CA	16	1	1	1	1	1	0	1	1	0	0	0	0	data scientist	na	M	86	142	25000000	50000000
6	Research Scientist	3.3	10000+	2014	Hospital	Health Care Services & Hospitals	Health Care	38	84	61.0	NY	7	0	0	0	0	0	0	0	0	0	0	0	0	other scientist	na	P	38	84	500000000	1000000000
7	Data Scientist	4.6	51 - 200	2009	Company - Private	Internet	Information Technology	120	160	140.0	NY	12	1	1	0	0	0	0	0	0	0	0	0	0	data scientist	na	na	120	160	100000000	500000000
8	Data Scientist	3.5	501 - 1000	2011	Company - Private	Other Retail Stores	Retail	126	201	163.5	CA	10	1	0	0	0	0	0	0	0	0	0	0	0	data scientist	na	na	126	201	1000000000	2000000000
9	Data Scientist	4.1	5001 - 10000	1968	Company - Public	Research & Development	Business Services	64	106	85.0	VA	53	0	0	0	0	1	0	0	0	1	0	0	0	data scientist	na	na	64	106	1000000000	2000000000

Last rows

	Job Title	Rating	Size	Founded	Type of ownership	Industry	Sector	Hourly	Lower Salary	Upper Salary	Avg Salary(K)	Job Location	Age	Python	aws	excel	sql	sas	scikit	tensor	hadoop	tableau	bi	job_title_sim	seniority_by_title	Degree	Min_Salary	Max_Salary	Min_Revenue	Max_Revenue
224	Quality Control Scientist III- Analytical Development	2.7	201 - 500	1961	Company - Private	Biotech & Pharmaceuticals	Biotech & Pharmaceuticals	0	48	113	80.5	MD	60	0	0	1	0	0	0	0	0	0	0	other scientist	na	na	48	113	25000000	50000000
225	Clinical Scientist, Clinical Development	3.8	51 - 200	2008	Company - Private	Biotech & Pharmaceuticals	Biotech & Pharmaceuticals	1	56	97	76.5	MA	13	0	0	1	0	0	0	0	0	0	0	other scientist	na	na	27	47	10000000	25000000
226	Manager, Safety Scientist, Medical Safety & Risk Management	3.8	501 - 1000	2008	Company - Public	Biotech & Pharmaceuticals	Biotech & Pharmaceuticals	0	68	125	96.5	MA	13	0	0	1	0	0	0	0	0	0	0	Data scientist project manager	na	na	68	125	50000000	100000000
227	Data Scientist	3.5	1 - 50	-1	School / School District	K-12 Education	Education	0	71	121	96.0	MO	21	0	0	1	1	0	0	0	1	0	0	data scientist	na	P	71	121	50000000	10000000
228	Data Scientist	3.4	51 - 200	1997	Company - Private	Consulting	Business Services	0	72	121	96.5	DC	24	1	0	0	0	0	0	1	0	0	0	data scientist	na	M	72	121	10000000	25000000
229	Data Scientist	4.3	201 - 500	1969	Company - Private	IT Services	Information Technology	0	51	88	69.5	AZ	52	1	0	1	1	1	1	0	0	0	0	data scientist	na	na	51	88	50000000	100000000
230	Data Scientist (Warehouse Automation)	3.8	201 - 500	2005	Subsidiary or Business Segment	Consumer Products Manufacturing	Manufacturing	0	79	127	103.0	CA	16	1	1	0	1	0	0	0	0	1	1	data scientist	na	M	79	127	25000000	50000000
231	Data Architect / Data Modeler	4.3	1001 - 5000	1999	Company - Public	Enterprise Software & Network Solutions	Information Technology	0	63	110	86.5	NY	22	0	1	1	1	0	0	0	0	0	0	data modeler	na	M	63	110	500000000	1000000000
232	Data Scientist	3.4	51 - 200	-1	Company - Private	IT Services	Information Technology	0	65	113	89.0	WA	21	0	0	0	0	0	0	0	0	0	0	data scientist	na	na	65	113	50000000	10000000
233	Data Engineer	3.9	201 - 500	2011	Company - Private	Internet	Information Technology	0	62	113	87.5	CA	10	1	1	1	1	0	0	0	0	0	0	data engineer	na	P	62	113	100000000	500000000

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Other Punctuation

Decimal Number

Dash Punctuation

Space Separator

Open Punctuation

Close Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Punctuation

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Space Separator

Dash Punctuation

Math Symbol

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Space Separator

Dash Punctuation

Other Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Other Punctuation

Decimal Number

Space Separator

Dash Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category